Fog Computing Vehicular Network Resource Management Based on Chemical Reaction Optimization

Author(s):  
Yupei Liu ◽  
Haijun Zhang ◽  
Keping Long ◽  
Huan Zhou ◽  
Victor C.M. Leung
Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3444 ◽  
Author(s):  
Cheol-Ho Hong ◽  
Kyungwoon Lee ◽  
Minkoo Kang ◽  
Chuck Yoo

Fog computing is a new computing paradigm that employs computation and network resources at the edge of a network to build small clouds, which perform as small data centers. In fog computing, lightweight virtualization (e.g., containers) has been widely used to achieve low overhead for performance-limited fog devices such as WiFi access points (APs) and set-top boxes. Unfortunately, containers have a weakness in the control of network bandwidth for outbound traffic, which poses a challenge to fog computing. Existing solutions for containers fail to achieve desirable network bandwidth control, which causes bandwidth-sensitive applications to suffer unacceptable network performance. In this paper, we propose qCon, which is a QoS-aware network resource management framework for containers to limit the rate of outbound traffic in fog computing. qCon aims to provide both proportional share scheduling and bandwidth shaping to satisfy various performance demands from containers while implementing a lightweight framework. For this purpose, qCon supports the following three scheduling policies that can be applied to containers simultaneously: proportional share scheduling, minimum bandwidth reservation, and maximum bandwidth limitation. For a lightweight implementation, qCon develops its own scheduling framework on the Linux bridge by interposing qCon’s scheduling interface on the frame processing function of the bridge. To show qCon’s effectiveness in a real fog computing environment, we implement qCon in a Docker container infrastructure on a performance-limited fog device—a Raspberry Pi 3 Model B board.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 6792-6800 ◽  
Author(s):  
Sahrish Khan ◽  
Hasan Ali Khattak ◽  
Ahmad Almogren ◽  
Munam Ali Shah ◽  
Ikram Ud Din ◽  
...  

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29106-29117
Author(s):  
Konstantinos Antonakoglou ◽  
Maliheh Mahlouji ◽  
Toktam Mahmoodi

Sign in / Sign up

Export Citation Format

Share Document